In general, the term segmentation refers to the identification of boundaries of biological units, such as cells, within a digital image. These boundaries separate each individual unit from others. The digital image may be obtained using a microscope. Weak or data driven segmentation algorithms may be used to define cell boundaries. For example, a watershed transform is one image processing technique that has been used for segmenting images of cells. With the watershed transform, a digital image may be modeled as a three-dimensional topological surface, where values of pixels (e.g., brightness or grey level) in the image represent geographical heights.
Due to variations in the histology of different tissue types, however, weak segmentation algorithms may not produce an accurate segmentation without significant adaptation and optimization to specific tissue type applications. For example, a weak segmentation algorithm may cause the image to be over-segmented (e.g., what appears as a single cell may actually be only a portion of a cell) or under-segmented (e.g., what appears as a single cell may actually be several different cells in combination). Furthermore, the image may not be properly segmented with a weak segmentation algorithm, in part, because a suitable segmentation parameter for one region of the image may not work well in other regions of the same image. Therefore, a weak segmentation algorithm may not be robust enough for segmentation of large numbers of cells having many morphological variations.
There has been previous work performed regarding cell analysis (see, e.g., Lindblad et al., Image analysis for automatic segmentation of cytoplasms and classification of rac1 activation, Cytometry A, 57(1):22-33 (2004); Wahlby et al., Sequential immunofluorescence staining and image analysis for detection of large numbers of antigens in individual cell nuclei, Cytometry, 47:32-51 (2002); Parvin et al., Biosig: An imaging bioinformatics system for phenotypic analysis, IEEE Transactions on Systems, Man and Cybernetics, Part B, 33:814-824 (2003); Mouroutis et al., Robust cell nuclei segmentation using statistical modeling, Bioimaging, Vol, 6:79-911998 (1998); Lin et al., A hybrid 3d watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks; Cytometry Part A, 56A(1):23-36 (2003); McCullough et al., 3D segmentation of whole cells and cell nuclei in tissue using dynamic programming, Biomedical Imaging: From Nano to Macro, ISBI 2007, 4th IEEE International Symposium on, pages 276-279 (2007); Wang et al., Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy, Bioinformatcs, Vol. 24, No. 1, pages 94-101 (2008)).
For example, Wang et al. proposed a method for cell segmentation and cycle estimation using an individual channel. In general, such method is based on machine learning methods using support vector machines from segmented images. In Lindblad et al., a method for cell segmentation was generally proposed based on nuclei and cytoplasm markers, where each cell has one nucleus. Nuclei segmentation was performed by selecting a global threshold value, and using the watershed algorithm. Once the nuclei regions were segmented, they were used as seed regions to segment the cytoplasm by applying the watershed algorithm.
A method for quantification of sequential immunofluorescence staining was proposed in Wahlby et al. This method is generally based on the quantification of the immunofluorescence staining only in the nuclei, where nuclei segmentation is a semi-automatic process, which involves human intervention. In Mouroutis et al., a statistical method for nuclei segmentation was proposed. This method typically includes defining a likelihood function, and to separate touching nuclei as a mixture of Gaussians distributions.
In general, different methods have been proposed for cell segmentation in 3D in confocal imaging, and mainly these methods focus in segmenting the cell nuclei. For example, in Lin et al., a method for 3D nuclei segmentation from confocal stacks was proposed, and the approach generally includes three steps. The first step is a pre-processing step, where noise is removed and segmented using global thresholding. The second step typically separates those nuclei which are touching by applying a 3D watershed algorithm using a gradient-weighted distance transform. The third step is a post-processing step, which is typically used as surface breaker. A method for 3D segmentation of whole cells was reported by McCullough et al. This method is designed to segment the nuclei objects in 3D, where cell boundaries are detected. However, there is no segmentation of 3D cells as a unit.
Thus, an interest exists for improved systems and methods for analyzing digital images of biological tissue samples. These and other inefficiencies and opportunities for improvement are addressed and/or overcome by the systems, assemblies and methods of the present disclosure.